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Hoffmann M, Willruth LL, Dietrich A, Lee HK, Knabl L, Trummer N, Baumbach J, Furth PA, Hennighausen L, List M. Blood transcriptomics analysis offers insights into variant-specific immune response to SARS-CoV-2. Sci Rep 2024; 14:2808. [PMID: 38307916 PMCID: PMC10837437 DOI: 10.1038/s41598-024-53117-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 01/28/2024] [Indexed: 02/04/2024] Open
Abstract
Bulk RNA sequencing (RNA-seq) of blood is typically used for gene expression analysis in biomedical research but is still rarely used in clinical practice. In this study, we propose that RNA-seq should be considered a diagnostic tool, as it offers not only insights into aberrant gene expression and splicing but also delivers additional readouts on immune cell type composition as well as B-cell and T-cell receptor (BCR/TCR) repertoires. We demonstrate that RNA-seq offers insights into a patient's immune status via integrative analysis of RNA-seq data from patients infected with various SARS-CoV-2 variants (in total 196 samples with up to 200 million reads sequencing depth). We compare the results of computational cell-type deconvolution methods (e.g., MCP-counter, xCell, EPIC, quanTIseq) to complete blood count data, the current gold standard in clinical practice. We observe varying levels of lymphocyte depletion and significant differences in neutrophil levels between SARS-CoV-2 variants. Additionally, we identify B and T cell receptor (BCR/TCR) sequences using the tools MiXCR and TRUST4 to show that-combined with sequence alignments and BLASTp-they could be used to classify a patient's disease. Finally, we investigated the sequencing depth required for such analyses and concluded that 10 million reads per sample is sufficient. In conclusion, our study reveals that computational cell-type deconvolution and BCR/TCR methods using bulk RNA-seq analyses can supplement missing CBC data and offer insights into immune responses, disease severity, and pathogen-specific immunity, all achievable with a sequencing depth of 10 million reads per sample.
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Affiliation(s)
- Markus Hoffmann
- Data Science in Systems Biomedicine, TUM School of Life Sciences, Technical University of Munich, Freising, Germany.
- Institute for Advanced Study, Technical University of Munich, Lichtenbergstrasse 2 a, 85748, Garching, Germany.
- National Institute of Diabetes, Digestive, and Kidney Diseases, Bethesda, MD, 20892, USA.
| | - Lina-Liv Willruth
- Data Science in Systems Biomedicine, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Alexander Dietrich
- Data Science in Systems Biomedicine, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Hye Kyung Lee
- National Institute of Diabetes, Digestive, and Kidney Diseases, Bethesda, MD, 20892, USA
| | | | - Nico Trummer
- Data Science in Systems Biomedicine, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Jan Baumbach
- Chair of Computational Systems Biology, University of Hamburg, Hamburg, Germany
- Computational BioMedicine Lab, University of Southern Denmark, Odense, Denmark
| | - Priscilla A Furth
- Institute for Advanced Study, Technical University of Munich, Lichtenbergstrasse 2 a, 85748, Garching, Germany
- National Institute of Diabetes, Digestive, and Kidney Diseases, Bethesda, MD, 20892, USA
- Departments of Oncology & Medicine, Georgetown University, Washington, DC, USA
| | - Lothar Hennighausen
- Institute for Advanced Study, Technical University of Munich, Lichtenbergstrasse 2 a, 85748, Garching, Germany
- National Institute of Diabetes, Digestive, and Kidney Diseases, Bethesda, MD, 20892, USA
| | - Markus List
- Data Science in Systems Biomedicine, TUM School of Life Sciences, Technical University of Munich, Freising, Germany.
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